Contributions:
Our main contribution is the introduction of a learning paradigm called cooperative learning, where agents that are trying to learn the same concepts share their current (potentially incomplete and different) models of the concepts with each other and accelerate each others learning. We propose using attributes as the common representation to communicate this information across agents. This allows the agents to communicate with each other even if each agent is learning from a different source of data using different sensors, even under privacy, security and/or bandwidth constraints. We believe that this approach may be among the very few that is applicable to this practical setting.
We show that cooperative learning leads to improvement in performance when compared to the performance of each agent learning independently. We show results on a synthetic dataset with varying amounts of noise and three standard real datasets of animals (Animals with Attributes [1]}), scenes (SUN [2]}), and objects (RGBD Objects [3]}).
